Challenges. The problem of deploying generative models in memory-constrained devices is hard. For example, detection of anomalies at the edge where devices typically have at most 8GB of RAM available [1]}. Moreover, battery-operated hardware makes it harder to run heavy analysis models such as deep autoencoders [2]}. In such systems, it is possible to handle the processing limitations by effective preemption and prolonged job execution. However, memory bottlenecks are much harder to solve due to the limited performance of virtual memory in network attached storage settings [3]}.
